区块链与机器学习:一项调查

IF 8.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Safak Kayikci, Taghi M. Khoshgoftaar
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引用次数: 0

摘要

区块链和机器学习是两种快速发展的技术,正越来越多地应用于各行各业。区块链技术为记录交易提供了一种安全透明的方法,而机器学习则通过分析大量数据实现了数据驱动决策。近年来,研究人员和从业人员一直在探索将这两种技术结合起来的潜在好处。在本研究中,我们将介绍区块链和机器学习的基本原理,然后讨论它们在金融、医疗、供应链和安全领域的综合应用,包括文献综述以及它们对该领域的贡献,如提高安全性、隐私性和去中心化。区块链技术可实现安全、透明的分散式记录保存,而机器学习算法可分析大量数据,从而获得有价值的见解。两者结合在一起,有可能通过自动化和可信的流程提高效率,实现数据驱动的决策,并通过减少漏洞和确保信息的完整性来加强安全措施,从而彻底改变各行各业。然而,在普遍使用区块链和机器学习之前,仍有一些重要的挑战需要应对,如安全问题、战略规划、信息处理和可扩展的工作流程。不过,在已发现的困难得到解决之前,区块链和机器学习的潜力将无法得到充分发挥。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Blockchain meets machine learning: a survey

Blockchain meets machine learning: a survey

Blockchain and machine learning are two rapidly growing technologies that are increasingly being used in various industries. Blockchain technology provides a secure and transparent method for recording transactions, while machine learning enables data-driven decision-making by analyzing large amounts of data. In recent years, researchers and practitioners have been exploring the potential benefits of combining these two technologies. In this study, we cover the fundamentals of blockchain and machine learning and then discuss their integrated use in finance, medicine, supply chain, and security, including a literature review and their contribution to the field such as increased security, privacy, and decentralization. Blockchain technology enables secure and transparent decentralized record-keeping, while machine learning algorithms can analyze vast amounts of data to derive valuable insights. Together, they have the potential to revolutionize industries by enhancing efficiency through automated and trustworthy processes, enabling data-driven decision-making, and strengthening security measures by reducing vulnerabilities and ensuring the integrity of information. However, there are still some important challenges to be handled prior to the common use of blockchain and machine learning such as security issues, strategic planning, information processing, and scalable workflows. Nevertheless, until the difficulties that have been identified are resolved, their full potential will not be achieved.

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来源期刊
Journal of Big Data
Journal of Big Data Computer Science-Information Systems
CiteScore
17.80
自引率
3.70%
发文量
105
审稿时长
13 weeks
期刊介绍: The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.
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